CARTO > Case Studies > Supply Chain Network Optimization & Cold Chain Transportation for SEUR

Supply Chain Network Optimization & Cold Chain Transportation for SEUR

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Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Functional Applications - Transportation Management Systems (TMS)
Applicable Industries
  • E-Commerce
  • Transportation
Applicable Functions
  • Logistics & Transportation
  • Product Research & Development
Use Cases
  • Autonomous Transport Systems
  • Transportation Simulation
Services
  • Data Science Services
About The Customer
SEUR is a pioneering parcel delivery company with a history spanning over 75 years. They are market leaders in Spain with three main lines of business: international, e-commerce, and B2B. SEUR has over 1.2 million customers and delivers more than 300,000 parcels every day with a fleet of 4,700 vehicles. The company was in need of a solution to optimize their cold transportation network, assess the current state of their network, quantify the impact of changes, and build an optimization model for their supply chain network design.
The Challenge
SEUR, a leading parcel delivery company in Spain, was facing challenges in optimizing their cold transportation network. The company was looking for a solution that would allow them to assess the current state of their network, identify areas of high demand, and determine if their distribution centers (DCs) were strategically located. They also wanted to quantify the impact of changes in their current network, such as the opening or closing of DCs and changes in delivery areas. Furthermore, SEUR was seeking to build an optimization model to identify where DCs should be located and design their transportation network (supply chain network design).
The Solution
SEUR collaborated with CARTO to leverage the expertise of their highly skilled Spatial Data Science team. By applying different Spatial Data Science techniques in an iterative way and adding complexity over time, they were able to provide meaningful insights and results with every step. The optimization result gave an average distance of 18.23 km/order, compared to the original 18.99 km/order. This improvement, considering the hundreds of thousands of orders SEUR delivers every year in cold transportation, could translate into very significant savings in terms of fuel and fleet size, and better customer service.
Operational Impact
  • The collaboration with CARTO and the application of Spatial Data Science techniques provided SEUR with a competitive advantage by leveraging the spatial components of many of the supply chain processes. The iterative approach and the gradual increase in complexity allowed for meaningful insights and results at every step. The optimization not only resulted in significant savings in terms of fuel and fleet size but also improved customer service. The ability to assess and optimize the supply chain processes has positioned SEUR for better strategic decision-making in the future.
Quantitative Benefit
  • The optimization resulted in a reduction of average distance per order from 18.99 km to 18.23 km.
  • For 500,000 orders, this improvement translates into 380,000 fewer kilometers.
  • The reduction in distance translates into significant savings in terms of fuel and fleet size.

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